Spatial Data Science Languages: commonalities and needs
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arXiv
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| Format: | Preprint |
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2025
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| author | Pebesma, Edzer Fleischmann, Martin Parry, Josiah Nowosad, Jakub Graser, Anita Dunnington, Dewey Pronk, Maarten Schouten, Rafael Lovelace, Robin Appel, Marius Abad, Lorena |
| author_facet | Pebesma, Edzer Fleischmann, Martin Parry, Josiah Nowosad, Jakub Graser, Anita Dunnington, Dewey Pronk, Maarten Schouten, Rafael Lovelace, Robin Appel, Marius Abad, Lorena |
| contents | Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modelling communities, and statistical models. The following set of insights have been formulated: (i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies; (ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those; (iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of {\em simple features}, (iv) managing communities and fostering diversity is a necessary, on-going effort, and (v) tools for cross-language development need more attention and support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16686 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatial Data Science Languages: commonalities and needs Pebesma, Edzer Fleischmann, Martin Parry, Josiah Nowosad, Jakub Graser, Anita Dunnington, Dewey Pronk, Maarten Schouten, Rafael Lovelace, Robin Appel, Marius Abad, Lorena Computation Programming Languages Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modelling communities, and statistical models. The following set of insights have been formulated: (i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies; (ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those; (iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of {\em simple features}, (iv) managing communities and fostering diversity is a necessary, on-going effort, and (v) tools for cross-language development need more attention and support. |
| title | Spatial Data Science Languages: commonalities and needs |
| topic | Computation Programming Languages |
| url | https://arxiv.org/abs/2503.16686 |